Sékou-Oumar Kaba

According to our database, Sékou-Oumar Kaba authored at least 13 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Accurate and scalable exchange-correlation with deep learning.

Giulia Luise, Chin-Wei Huang, Thijs Vogels, Derk P. Kooi, Sebastian Ehlert, Stephanie Lanius, Klaas J. H. Giesbertz, Amir Karton, Deniz Gunceler, Megan Stanley, Wessel P. Bruinsma, Lin Huang, Xinran Wei, José Garrido Torres, Abylay Katbashev, Rodrigo Chavez Zavaleta, Bálint Máté, Sékou-Oumar Kaba, Roberto Sordillo, Yingrong Chen, David B. Williams-Young, Christopher M. Bishop, Jan Hermann, Rianne van den Berg, Paola Gori-Giorgi

CoRR, June, 2025

Symmetry-Aware Generative Modeling through Learned Canonicalization.

Kusha Sareen, Daniel Levy, Arnab Kumar Mondal, Sékou-Oumar Kaba, Tara Akhound-Sadegh, Siamak Ravanbakhsh

CoRR, January, 2025

SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models.

Daniel Levy, Siba Smarak Panigrahi, Sékou-Oumar Kaba, Qiang Zhu, Kin Long Kelvin Lee, Mikhail Galkin, Santiago Miret, Siamak Ravanbakhsh

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Improving Equivariant Networks with Probabilistic Symmetry Breaking.

Hannah Lawrence, Vasco Portilheiro, Yan Zhang, Sékou-Oumar Kaba

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

On the Identifiability of Causal Abstractions.

Xiusi Li, Sékou-Oumar Kaba, Siamak Ravanbakhsh

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2023

Symmetry Breaking and Equivariant Neural Networks.

Sékou-Oumar Kaba, Siamak Ravanbakhsh

CoRR, 2023

Equivariant Adaptation of Large Pretrained Models.

Arnab Kumar Mondal, Siba Smarak Panigrahi, Sékou-Oumar Kaba, Sai Rajeswar, Siamak Ravanbakhsh

CoRR, 2023

Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks.

Daniel T. Levy, Sékou-Oumar Kaba, Carmelo Gonzales, Santiago Miret, Siamak Ravanbakhsh

CoRR, 2023

Equivariant Adaptation of Large Pretrained Models.

Arnab Kumar Mondal, Siba Smarak Panigrahi, Oumar Kaba, Sai Mudumba, Siamak Ravanbakhsh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Equivariance with Learned Canonicalization Functions.

Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh

Proceedings of the International Conference on Machine Learning, 2023

2022

Equivariant Networks for Crystal Structures.

Sékou-Oumar Kaba, Siamak Ravanbakhsh

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Prediction of Large Magnetic Moment Materials With Graph Neural Networks and Random Forests.

Sékou-Oumar Kaba, Benjamin Groleau-Paré, Marc-Antoine Gauthier, André-Marie Tremblay, Simon Verret, Chloé Gauvin-Ndiaye

CoRR, 2021

Gradient Starvation: A Learning Proclivity in Neural Networks.

Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021